This paper presents a fully data-driven 3-D path-following framework for autonomous underwater vehicles (AUVs), a representative class of underwater field robotics, based on Data-Enabled Predictive Control (DeePC). The approach eliminates explicit hydrodynamic modeling by exploiting measured input-output trajectories to predict and optimize future system behavior. Classic DeePC is employed for heading control, while a cascaded DeePC architecture with loop-frequency separation is proposed for depth regulation, extending DeePC to plants whose dominant output evolves significantly slower than the actuator bandwidth. For 3-D waypoint path following, the Adaptive Line-of-Sight (ALOS) guidance law is extended to a predictive multistep formulation (PALOS) that supplies the horizon-consistent reference required by receding-horizon predictive controllers. All methods are validated in high-fidelity 6 degrees of freedom simulation on the REMUS 100 AUV under nominal operation, ocean-current disturbances, operation beyond the data regime, and 3-D waypoint path following, consistently outperforming the corresponding state-of-the-art benchmarks. In 3-D waypoint path following, the framework reduces cross-track error by approximately 28% relative to the ALOS-PI/PID baseline.

Cost-efficient methods and technologies for environmental monitoring is an enabler for holistic decision making and sustainable management of natural resources. The information value from monitoring surveys with robotic sensor platforms such as AUVs and UAVs can increase if domain knowledge is exploited when selecting the sampling locations. We investigate how to maximize the information value of surveys targeting dispersible substances by adjusting the survey pattern in accordance with the direction and strength of the wind or water currents. Samples are acquired with a range of different patterns, deployed in a large number of randomly generated emission scenarios in a Monte Carlo framework. The information value from each survey is used as a performance metric to identify opportune pattern configurations. Among the results is the impact that the orientation of the survey pattern relative to the direction of the wind has on the information value, and that given proper orientation the line spacing can be wider. The results are used to suggest guidelines for robotic environmental monitoring surveys.

Operations of multiagent systems consisting of autonomous underwater vehicles (AUVs) and autonomous surface vessels (ASVs) offer a cost-effective solution to a wide range of marine applications, such as mapping and monitoring of the oceans. This article proposes a switching controller for tracking one or multiple AUVs with an ASV. Using three control modes, the controller ensures that: 1) each AUV eventually gets tracked and aided; 2) collisions with the AUVs are avoided; and 3) the ASV stops active propulsion whenever practical in order to conserve energy and reduce acoustic noise caused by the propulsion system. The switching of modes is based on the relative geometry between the ASV and the AUVs and backward reachable sets (BRSs). The switching controller is experimentally demonstrated with satisfying results in a series of field experiments in the Trondheim Fjord, where up to three AUVs executing missions simultaneously are tracked and aided by one ASV.

Risk awareness and assessment are fundamental aspects of human cognition and situational awareness, and play crucial roles in problem solving and decision-making. In this article, we present a novel methodology for integrated risk modeling and path planning in robotics mimicking these human processes. This approach creates a holistic geospatial data structure of risk, showing what may go wrong, where and when it is more likely, and the potential causes and consequences; all of which may be used as input to planning and decision-making algorithms for improved robotic autonomy. First, a hazard analysis of the operation is performed, with the objective of analyzing possible hazardous events, their causal factors, and potential consequences. This knowledge is then incorporated into a Bayesian belief network for estimating the risk at a particular point in space and time. Two methods for path planning taking these results as input are proposed: first, the risk-based path planner, and second, the risk-based traveling salesperson, both of which can balance the tradeoffs between risk and reward related to the mission objectives. We demonstrate the novel methodology with a real case study: seabed survey of the Tautra coral reef in Norway using an autonomous underwater vehicle (AUV), capitalizing on data from previous field operations. The case study shows that the AUV adapts its mission based on the perceived and assessed risk. By combining methods from robotics, artificial intelligence, risk science, and geoinformatics this work provides an interdisciplinary and novel contribution to enhanced robotic autonomy.

Optical imaging for identifying targets of interest is an important operational phase in many applications of autonomous underwater vehicles (AUVs). However, underwater optical imaging is challenging due to limited visibility and rugged terrain. To counter the low visibility, the AUV needs to fly at a low altitude over the targets. This results in a small optical footprint and corresponding small margins for error in maneuvering and navigation accuracy, in addition to a significant risk of collision with the seabed. In this paper, we propose a planning algorithm that adapts the mission to in-situ knowledge for safe and robust optical inspection of seafloor objects. By using automatic target recognition on data from a multibeam echosounder with a wider field of view, the algorithm checks whether the target was within the camera footprint. For targets that were deemed outside, i.e., missed, the plan is updated with the corrected object positions. This significantly increases the probability of successful optical inspection in a reduced amount of time, as well as reduces the risk of collision. The proposed method has been demonstrated in sea experiments using a HUGIN AUV.

This paper proposes a formation control method for two underactuated unmanned surface vessels (USVs) to follow curved paths in the presence of ocean currents. By uniting a line-of-sight (LOS) guidance law and the null-spacebased behavioral control (NSB) framework, we achieve curved path following of the barycenter, while maintaining the desired vessel formation. The closed-loop dynamics are investigated using cascaded systems theory, and it is shown that the closed-loop system is USGES and UGAS, while the underactuated sway dynamics remains bounded. Both simulation and experimental results are presented to verify the theoretical results.

Avoiding collisions is a crucial ability for unmanned vehicles. In this paper, we present the constant avoidance angle algorithm, a reactive method for collision avoidance. It can be used to avoid both static and moving obstacles by making the vehicle keep an avoidance angle between itself and the obstacle edge. Unlike many other algorithms, it requires neither knowledge of the complete obstacle shape nor that the vehicle follows a desired speed trajectory. Instead, safe vehicle headings are provided at the current vehicle speed. Thus, the speed can be used as an input to the algorithm, which provides flexibility and makes the approach suitable for a wide range of vehicles, including vehicles with a limited speed envelope or high acceleration cost. We demonstrate this by applying the algorithm to a marine vehicle described by a full kinematic and dynamic model in three degrees of freedom. We specifically consider vehicles with underactuated sway dynamics, where the vehicle velocity contains a component that cannot be directly controlled. Such dynamics can be highly detrimental to the performance of collision avoidance algorithms and need to be included in the design and analysis of control systems for such vehicles. In this paper, we compensate for the underactuation by including these dynamics in the underlying analysis and control design. We provide a mathematical analysis of sparse obstacle scenarios, where we derive conditions under which safe avoidance is guaranteed, even for underactuated vehicles. We furthermore show how the modular nature of the algorithm enables it to be combined both with a target reaching and a path following guidance law. Finally, we validate the results both through numerical simulations and through full-scale experiments aboard the R/V Gunnerus.

This paper presents a 3D reactive collision avoidance algorithm for vehicles with underactuated dynamics. The underactuated states cannot be directly controlled, but are controlled indirectly by steering the direction of the vehicle's velocity vector. This vector is made to point a constant avoidance angle away from the obstacle, thus ensuring collision avoidance, while the forward speed is kept constant to maintain maneuverability. We choose an optimal pair of desired heading and pitch angles during the maneuver, thus taking advantage of the flexibility provided by operating in 3D. The algorithm incorporates limits on both the allowed pitch angle and the control inputs, which are limits that often are present in practical scenarios. Finally, we provide a mathematical proof that the collision avoidance maneuver is safe, and support the analysis through several simulations.

Autonomous underwater vehicles (AUVs) are robotic platforms that are commonly used to map the sea floor, for example for benthic surveys or for naval mine countermeasures (MCM) operations. AUVs create an acoustic image of the survey area, such that objects on the seabed can be identified and, in the case of MCM, mines can be found and disposed of. The common method for creating such seabed maps is to run a lawnmower survey, which is a standard method in coverage path planning. We are interested in exploring alternate techniques for surveying areas of interest, in order to reduce mission time and/or assess feasible actions, such as finding a safe path through a hazardous region. In this paper, we use Gaussian Process (GP) regression to build models of seabed complexity data. We evaluate five commonly used kernels to assess modeling performance. Our results show that an additive Matérn kernel is most suitable for modeling seabed complexity data. On top of the GP model, we use adaptations of two standard path planning methods, A* and RRT*, to find potentially safe paths for marine vessels through the modeled areas. We evaluate the planned paths in terms of length and complexity, and run a vehicle dynamics simulator to assess potential performance by a marine vessel. Finally, we propose three approaches for on-line AUV survey path planning. The AUV decides on-line where to survey, based on the GP model it is creating and the GP’s uncertainty along potential safe paths for marine vessels. Our simulation results show that using onboard on-line AUV survey planning greatly reduces survey time needed by the AUV compared to running a full lawnmower survey with off-line vessel path planning.

This paper presents an algorithm that makes an underactuated marine vehicle follow a straight line path while in the presence of a constant ocean current. When following the path, the vehicle maintains a desired surge speed which is measured relative to the water, and which may be constant or time-varying. The algorithm is an integral line-of-sight guidance law where the lookahead distance is designed to depend linearly on the desired relative surge speed of the vehicle. This dependency makes it possible to keep the maneuvering demands of the vehicle limited, even when the vehicle surge speed is large. It is shown that if the desired relative surge speed is constant along the path, the resulting error dynamics has a uniformly semiglobally exponentially stable equilibrium at the origin, thus achieving the path following and velocity control objectives. Furthermore, in the case of a general, time-varying desired speed trajectory, it is shown that the solutions of the system remain bounded. The results are supported by simulations, as well as experiments with an unmanned surface vehicle.

This paper presents a reactive collision avoidance algorithm, which avoids both static and moving obstacles by keeping a constant avoidance angle between the vehicle velocity vector and the obstacle. In particular, we consider marine vehicles with underactuated sway dynamics, which cannot be directly controlled. This gives an underactuated component in the vehicle velocity, which the proposed algorithm is designed to compensate for. The algorithm furthermore compensates for the obstacle velocity. Conditions are derived under which the sway movement is bounded and collision avoidance is mathematically proved. The theoretical results are supported by simulations. The proposed algorithm makes only limited sensing requirements on the vehicle, is intuitive and suitable for a wide range of vehicles. This includes vehicles with heavy forward acceleration constraints, which is demonstrated by applying the algorithm to a vehicle with constant surge speed.

This paper analyzes an integral line-of-sight guidance law applied to an underac-tuated underwater vehicle. The vehicle is rigorously modeled in 5 degrees of freedom using physical principles, and it is taken into account that the vehicle is not necessarily neutrally buoyant. The closed-loop dynamics of the cross-track error are analyzed using nonlinear cascaded systems theory, and are shown to achieve uniform semiglobal exponential stability. Hence, the integral line-of-sight guidance law compensates for the lack of neutral buoyancy, and it is no longer necessary to assume that the vehicle is perfectly ballasted. The exponential convergence properties of the guidance law are demonstrated in simulations of an autonomous underwater vehicle.

In April 2016, NTNU, FFI, Kongsberg Seatex, LSTS and Maritime Robotics set up an experiment to explore their capability to combine the research vessel Gunnerus, the AUV Hugin, the UAV X8 and the USV Mariner in a network of heterogeneous unmanned vehicles. Communication, manoeuvring, onboard processing and operational complexity are essential components in such networks. To provide communication the MBR broadband radio system was implemented. To show the capabilities of the system proposed, a scenario with seabed mapping and target recognition was defined. The experiment made it apparent that these networks has the potential of significantly saving cost for data collection in marine research and management by reducing ship time. To fully unlock the potential of networks of heterogeneous unmanned vehicles, the missions of each vehicle need to be more integrated.

This paper proves that an integral line-of-sight guidance law for path following control of underactuated marine vessels provides uniform semiglobal exponential stability. The stability result is stronger than what has been proved in previous literature, with stronger convergence properties and more robustness. The analysis is based on the 3-dimensional maneuvering control model of marine vessels, which describes both surface vessels and underwater vehicles moving in a horizontal plane. Both the kinematics and dynamics of the system is taken into account, as well as disturbances from constant and irrotational ocean currents. Simulation results are presented to validate the theoretical analysis.

This paper presents a concept and algorithms to detect, classify and identify mine-like objects within a single mission with an autonomous underwater vehicle. The autonomous mine hunting concept has been developed for the HUGIN series of vehicles. First, the operation area is surveyed either with a synthetic aperture sonar or a side-scanning sonar. During the survey, mine-like objects are detected and classified in the data using algorithms for automatic target recognition. When the survey is complete, a framework for autonomy initiates a fusion of the targets and starts the automatic planning of a mission plan for target identification. The autonomous mine hunting concept is a part of the development of a framework for advanced autonomy on HUGIN, the HUGIN autonomy layer. Implementation of this framework will reduce the risk of long-term AUV missions, and will provide intelligent vehicle behavior not only to re-inspect interesting objects and areas, but also to preserve vehicle safety, navigational accuracy and mission goals.

External inspection of seafloor pipelines is presently carried out with towed or remotely operated vehicles (ROV) operated from advanced offshore vessels, making it a time-consuming and complex activity. In co-operation with Kongsberg Maritime, FFI is developing a concept based on autonomous underwater vehicles (AUV) that has potential as a cost-effective augmentation of the ROV-based inspection. The concept was successfully demonstrated with a HUGIN 1000 AUV along a 30 km long section of an oil & gas pipeline on the western coast of Norway in February 2011. The AUV surveys the pipeline unaccompanied by the surface ship, which is then relieved to perform parallel inspection with ROV. The AUV must follow the pipeline within a cross-track range interval given by the sensor swaths. Preprogrammed mission paths may then be inadequate, in case of large prior uncertainties in pipe route and drift in vehicle position estimates. To ensure optimal pipe following, the pipeline should be automatically recognized in the sensor data and the vehicle path adjusted accordingly. This paper addresses the detection and tracking tasks when the AUV is travelling 50-100 meters to the side of the pipeline, imaging the pipeline and its surrounding seafloor with long-range, high resolution synthetic aperture sonar (SAS).

In this paper we present a planning algorithm for identification missions in an autonomous mine countermeasure scenario using AUVs. The concept of autonomous MCM identification missions is to first perform an autonomous survey of the mission area, during which an automatic target recognition (ATR) algorithm is run on the collected side-scan data. The ATR software detects possible contacts in the data, and classifies them as either clutter or a selection of mine classes. When the detection and classification are completed, the contact list is processed. In this processing contact with low detection confidence and classification confidence are discarded, while the rest are sent through a fusion process. In this process close contacts are fused together and their position averaged. After the contacts have been processed the list is sent to the identification planner. The identification planner will then create a mission plan with the goal of obtaining optical images of the reported contacts. To allow for deep water operation where surfacing for GPS position updates is impractical, and to allow for covert operations, HUGIN real-time terrain navigation is used, enabling the vehicle to update its navigation during the identification mission using a map created during the survey.

This thesis addresses attitude synchronization in spacecraft formations. In addition to theoretical results the thesis presents the design and implementation of an experimental platform for spacecraft attitude synchronization. The first part of the thesis gives a general introduction to spacecraft formation flying with possible applications and current proposed and scheduled missions, and background information on relevant related work presented in the literature. We also give some necessary mathematical preliminaries, included for the sake of completeness and to give the reader an introduction to the notation and mathematical models required to grasp the theoretical contents. The theoretical results are presented in four separate chapters based on published and submitted conference papers, journal papers and a book chapter. In the final part of the thesis we present the design and implementation of an experimental platform for spacecraft attitude synchronization. The platform is based on two spherical autonomous underwater vehicles, internally actuated by means of reaction wheels. In Chapter 4 we present an adaptive external synchronization scheme for a spacecraft actuated by means of reaction wheels. The controller uses the quaternion parameterization of attitude, and is proven to be globally exponentially stable on S(3)_R3 in the known parameter case and globally convergent when using adaptive feedback. In Chapter 5 we present a 6 degrees of freedom (6-DOF) synchronization scheme for a deep space formation of spacecraft. In the design, which is referred to as a mutual synchronization scheme, feedback interconnections are designed in such a way that the spacecraft track a time varying reference trajectory while at the same time keep a prescribed relative attitude and position. The closed-loop system is proven uniformly locally asymptotically stable, with an area of attraction which covers the complete state-space, except when the spacecraft attains an attitude where the inverse kinematics are undefined. The proof is carried out using Matrosov’s Theorem. The contribution of Chapter 6 is a PID+ backstepping controller, as a solution to the problem of coordinated attitude control in spacecraft formations. The control scheme is based on quaternions and modified Rodriguez parameters as attitude representation of the relative attitude error. Utilizing the invertibility of the modified Rodriguez parameter kinematic differential equation, a globally exponentially stable control law for the relative attitude error dynamics is obtained through the use of integrator augmentation and backstepping. The contribution of Chapter 7 is the design of an observer-controller output feedback scheme for relative spacecraft attitude. The scheme is developed for a leader-follower spacecraft formation, where the leader is assumed to be controlled by an asymptotically stable tracking controller. Furthermore we assume that the follower has knowledge about its own attitude and angular velocity in addition to the relative attitude with respect to the leader. Since we do not know the angular velocity and acceleration of the leader, we design an error observer. The contribution of Chapter 8 is the design of AUVSAT, an experimental platform for relative spacecraft attitude synchronization. We present the mechanical and electrical network design of the vehicles. In addition an overview is given of the control hardware, including sensors, actuators and computers, and software designed to control the vehicle platforms. The contribution of Chapter 9 is the experimental validation of control algorithms for relative attitude synchronization in a two satellite leader-follower formation. We present experimental results for the PID+ backstepping design of Chapter 6 and the output feedback design in Chapter 7.